In recent years, the data warehouse industry has witnessed decreased use of indexing but increased use of compression and clustering of data facilitating efficient data access and data pruning in the query processing area. A classic example of data pruning is the partition pruning, which is used when table data is range or list partitioned. But lately, techniques have been developed to prune data at a lower granularity than a table partition or sub-partition. A good example is the use of data pruning structure called zone map. A zone map prunes zones of data from a table on which it is defined. Data pruning via zone map is very effective when the table data is clustered by the filtering columns. The database industry has offered support to cluster data in tables by its local columns, and to define zone maps on clustering columns of such tables. This has helped improve the performance of queries that contain filter predicates on local columns. However, queries in data warehouses are typically based on star/snowflake schema with filter predicates usually on columns of the dimension tables joined to a fact table. Given this, the performance of data warehouse queries can be significantly improved if the fact table data is clustered by columns of dimension tables together with zone maps that maintain min/max value ranges of these clustering columns over zones of fact table data. In recognition of this opportunity of significantly improving the performance of data warehouse queries, Oracle 12c release 1 has introduced the support for dimension based clustering of fact tables together with data pruning of the fact tables via dimension based zone maps.

Multimedia applications have undergone tremendous changes in the recent past that they have called for a scalable and reliable processing and storage framework. Image processing algorithms such as pornographic content detection becomes a lot more challenging in terms of accuracy, recall, and speed when run on billions of images. This paper presents the design and implementation of a hybrid-distributed architecture that uses Hadoop distributed file system for storage and Map/Reduce paradigm for processing images, crawled from the web. This architecture combines the power of Hadoop framework when there is a need to parallelize the task, as Map/Reduce jobs and uses stand alone crawler nodes to fetch relevant contents from the web. Evaluations on real world web data indicate that the system can store and process billions of images in few hours.

The Cell Broadband Engine™ is a new heterogeneous multi-core processor from IBM, Sony, and Toshiba. It contains eight co-processors, called Synergistic Processing Elements (SPEs), which operate directly on distinct 256 KB local stores, and also have access to a shared 512 MB to 2 GB main memory. The combined peak speed of the SPEs is 204.8 Gflop/s in single precision and 14.64 Gflop/s in double precision. There is, therefore, much interest in using the Cell BE™ for high performance computing applications. However, the unconventional architecture of the SPEs, in particular their local stores, creates some programming challenges. We describe our implementation of certain core features of MPI, such as blocking point-to-point calls and collective communication calls, which can help meet these challenges, by enabling a large class of MPI applications to be ported to the Cell BE™ processor. This implementation views each SPE as a node for an MPI process. We store the application data in main memory in order to avoid being limited by the local store size. The local store is abstracted in the library and thus hidden from the application with respect to MPI calls. We have achieved bandwidth up to 6.01 GB/s and latency as low as 0.41 ms on the ping-pong test. The contribution of this work lies in (i) demonstrating that the Cell BE™ has good potential for running intra-Cell BE™ MPI applications, (ii) enabling such applications to be ported to the Cell BE™ with minimal effort, and (iii) evaluating the performance impact of different design choices.

Hydraulic machines used in a number of applications are highly non-linear systems. Besides the dynamic coupling between the different links, there are significant actuator nonlinearities due to the inherent properties of the hydraulic system. Automation of such machines requires the robotic machine to be at least as productive as a manually operated machine, which in turn makes the case for performing tasks optimally with respect to an objective function (say) composed of a combination of time and fuel usage. Optimal path computation requires fast machine models in order to be practically usable. This work examines the use of memory-based learning in constructing the model of a 25-ton hydraulic excavator. The learned actuator model is used in conjunction with a linkage dynamic model to construct a complete excavator model which is much faster than a complete analytical model. Test results show that the approach effectively captures the interactions between the different actuators.

Dante II is a tethered mobile robot designed for volcano exploration. It has 6 pantographic legs, and is a rappelling robot that uses a tether to support itself on steep terrain, just as a mountaineer uses a climbing rope. The tether is connected to a generator and satellite communication station located at the volcano's rim. The satellite station relays data to and from remotely located operators. This paper describes the issues faced in the design of the tethering system of Dante II, and the approach used to address these issues.